Transformers are a relatively new class of machine learning models compared to CNNs or LSTMs. For their reliable and trustworthy use, we must understand the black-box nature of these models and explain their decisions. Many approaches to interpreting and explaining model predictions are post hoc, that is, the explanation is made after the fact. Post hoc approaches apply to almost any model. Many of these approaches provide visually appealing results, but might not accurately explain what is occurring inside the model [1]}. One possible way to incorporate explanations and interpretability into the model itself, rather than being approximated after the fact. There now exist multiple time-series Transformers that are inherently interpretable [2]}, [3]}, [4]}. These models can produce explanations which allow for better interpretations of results and greater user trust.
